Usually, fracture networks, especially in tight-gas sands, are exploited for efficient hydrocarbon recovery from the reservoirs [1, 2]. Sometimes, hydrocarbon recovery completely relies on the exploitation of the natural fracture networks in the subsurface.
Several geophysical techniques are available for characterizing fracture networks in the subsurface and each has its own advantages and disadvantages. All these techniques can be divided into two broad categories: (1) direct measurements and (2) indirect (or remote) measurements. An example of a direct measurement is a well-bore based method. Usually a geophysical instrument is sent into the well-bore and the geophysical tool measures the subsurface properties such as seismic velocities. These subsurface data are used to predict the fracture properties of the subsurface [3]. Although these types of techniques are very reliable, they provide fracture properties only at the well-bore location. Away from the well-bore, these methods cannot be trusted for fracture characterization.
An example of an indirect or remote measurement is surface seismic method. Surface seismic methods are one of the most common techniques for subsurface imaging. Seismic P- and S-waves are the two types of seismic waves that are used for this purpose. A P-wave source such as dynamite is used to excite P-wave energy which travels down the subsurface and reflects back both as P- and S-waves. These reflected waves are captured by surface receivers. These reflected energies are used to generate subsurface images and to derive other subsurface properties. P-waves are recorded by vertically oriented receivers and S-wave energies are recoded by horizontally oriented receivers. The reflected P-wave energies are traditionally called PP modes and the reflected S-wave energies are called PS or converted-wave modes.
In the past, geophysicists have proposed and implemented a number of techniques to characterize fractures using surface seismic data. Fractured reservoirs are known to behave as an azimuthally anisotropic medium on the scale of seismic wavelengths [4]. Ruger and Tsvankin [5] showed that PP-reflectivity in fractured reservoirs varies with the fracture azimuth. They also gave analytical expressions for PP-reflectivity which could be used to estimate fracture intensity (or density) of the medium. Methods based on this property of the PP-mode are called AVAZ-based methods.
S-waves, also called shear waves, travelling through a fractured medium split into fast (S1) and slow (S2) modes. The particle motions of S1- and S2-waves are polarized parallel and perpendicular to fracture strike, respectively. S-waves polarized parallel to fractures (S1) have a greater velocity than the S-waves polarized perpendicular to fractures (S2). The difference between the fast and slow S-wave velocities is directly proportional to fracture density; i.e. the larger the fracture density, the larger the difference between velocities. This phenomenon is called S-wave birefringence [6]. A number of fracture characterization methods have been proposed based on this property of S-wave [20].
Alford [7, 15] proposed a technique for a vertical seismic profile (VSP) geometry that includes rotating, in a synchronic way, source and receiver geophone by linearly combining the two polarizations. The method requires two orthogonal source components and two orthogonal receiver components. A 2×2 data matrix is formed and the energy in the off-diagonal terms are minimized by tensor rotation. The angle at which the off-diagonal energy is minimized is the azimuth of the fractures in the subsurface. The main disadvantage of this method is that the estimated fracture properties are only reliable at the VSP location.
Winterstein and Meadows [8] reported that the subsurface rarely has only one fractured layer; instead, many fractured layers with varying fracture orientations are more common They proposed a coarse-layer stripping technique to deal with this problem. The following is the idea behind their method; first rotate and find the time-difference between S1 and S2 for the arrivals from the bottom of the first fractured layer, then subtract the one- or two-way time (depending on whether the data is VSP or surface seismic) from the arrivals from the bottom of next fractured layer and correct for time lag by the first fractured layer. The procedure is repeated for subsequent fractured layers.
Gaiser [9, 14] extended the method of Alford [7, 15] to characterize subsurface fractures using surface seismic PS data. Unlike the method of Alford [7, 15], Gaiser's technique uses surface seismic data for fracture characterization. Gaiser's method can also perform coarse layer-stripping in the presence of multiple fractured layers.
Bansal et al. [18] developed a method to perform true-amplitude layer-stripping using surface seismic PS data. Unlike the method of Gaiser [9, 14], Bansal's method perform a scan of offset and azimuths of the surface seismic data to obtain the optimum dataset to perform layer stripping. This method also produces true-amplitude fast and slow S-waves which can be used to perform seismic inversion to in order to predict the lithology of the subsurface.
All the previous fracture characterization methods require the data to be divided in several time windows before layer-stripping is performed to compute the slow S-wave time-lag and the fracture orientation. The estimated S-wave time-lag represents the cumulated traveltime difference between the fast and slow S-waves in a given time window. In order to locate the highly fractured zones, length of the analysis time windows should be as short as possible such that the estimated S-wave time-lag represents the local anisotropy strength and not a cumulative effect.
In the field data, however, the length of time windows depends on the signal-to-noise ratio. The time-difference between the fast S-wave (S1) and slow S-wave (S2) modes are computed by cross-correlating the reflection events in the given window. If the time windows are small and the signal-to-noise ratio is not sufficient enough, cross-correlation process becomes unstable. This limitation on the conventional layer-stripping usually forces one to choose large time windows for analysis. This problem becomes more aggravated in land converted-wave (PS) and pure S-wave data which are notoriously noisy. The noise precludes identifying highly anisotropic zones from moderately anisotropic or isotropic zones.
Thus, there is a need for improvement in this field.